Machine Learning Challenge: Day 8
feature engineering in machine learning Feature selection Feature selection is the process of identifying which features are most important in your data. If you can find a way to remove unimportant features, then it's possible that you'll be able to improve the performance of your machine-learning model. Feature selection can be done via many different techniques, and there are many different ways to go about it. In this post, we will cover some of these techniques and explain why they work so well for feature selection: Collinear Columns Collinear columns are two columns that are highly correlated. If you have several input variables, and one of the inputs is strongly related to another input, it can be hard to tell which is causing the effect. For example, if you have two features (A and B) with a high correlation between them, then it’s hard to tell whether A causes B or vice versa. In order for us humans to use our eyesight effectively in everyday life—like w...